It is common to divide federated learning cases in two distinct categories [1]}. In horizontal FedML the data is assumed to be partitioned by example, i.e. the feature space are assumed to be (nearly) the same between different participants (for example different scientific instruments sharing performance metrics but operating in different conditions and owned by different companies). In vertical FedML the data is assumed to be partitioned by features, i.e. different participants might hold different types of information about the same example (for example a bank and an insurance company holding different types of data about the same customer). The majority of presented solutions fall under horizontal federated learning settings by exploiting Neural Networks (NN) technology and perform synchronous batch-based training rounds [2]}, [3]}. Recently, Bonawitz et al. [4]} proposed a high-level design of a scalable cross-device FedML framework based on TensorFlow where the focus is on horizontal FedML training. Kewei et al. [5]} introduced a lossless federated training scheme based on secure XGBoost for vertical FedML. This study focuses on horizontal FedML applications.
